
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "141"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 93 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 89 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.917124	-0.353896	0.697590	2.865073	4.901251	1.424712	1.319042	2.704330	0.7956	0.7943	0.2305	12.315580	10.769969
2459593	digital_ok	0.00%	0.00%	0.00%	0.00%	2.15%	0.00%	2.110876	1.548239	2.218808	3.857649	2.586009	2.178171	1.182904	1.102890	0.7490	0.7387	0.2433	2.618858	2.615134
2459592	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	0.696471	-0.101752	0.599585	0.213570	2.000801	-1.222038	2.241276	-0.099442	0.0427	0.0310	0.0025	nan	nan
2459591	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.064696	1.045030	3.256528	5.120498	4.007594	1.186997	1.673141	0.338801	0.6813	0.6968	0.3482	4.891889	4.720344
2459590	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.390805	1.074949	2.176660	4.190024	5.352311	1.547482	0.719155	-0.384357	0.6808	0.6955	0.3430	5.120091	5.116462
2459589	digital_ok	0.00%	0.00%	0.00%	0.00%	2.69%	0.00%	2.106482	1.374549	0.448494	3.275510	3.765882	1.599607	1.385612	2.039089	0.7230	0.7162	0.2709	1.923657	1.936374
2459588	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.088482	1.190492	2.522220	5.115261	2.777838	1.155723	1.389159	1.661226	0.7919	0.7729	0.2093	9.416795	6.945280
2459587	digital_ok	100.00%	-	-	-	100.00%	0.00%	348.542021	354.407865	inf	inf	24.254429	105.385033	198.293088	498.376133	nan	nan	nan	0.000000	0.000000
2459586	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.864453	0.058210	1.524319	0.474697	5.113056	-0.274879	3.404453	-0.184627	0.6635	0.6720	0.3445	5.047335	4.547983
2459585	digital_ok	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	7.298516	0.529492	3.822783	0.335335	5.347925	-0.425008	4.698272	-0.278367	0.0411	0.0371	0.0033	0.000000	0.000000
2459584	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.835255	2.502751	0.030839	4.201016	-0.504956	20.321778	9.235087	8.438152	0.6242	0.6366	0.3452	0.000000	0.000000
2459583	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.780651	0.848517	-0.693126	2.999822	4.168318	0.859174	0.595939	0.410113	0.6837	0.6955	0.3615	0.000000	0.000000
2459582	digital_ok	0.00%	0.00%	0.00%	0.00%	44.86%	1.08%	0.804822	0.756730	-0.686502	2.729792	3.976118	1.021931	0.310403	0.606764	0.6864	0.6963	0.3626	1.588423	1.507338
2459581	digital_ok	0.00%	0.00%	0.00%	0.00%	3.24%	16.22%	1.299234	1.562175	-0.835811	3.154635	2.954253	1.736660	0.215075	0.195767	0.6963	0.7049	0.3562	1.904731	1.839496
2459580	digital_ok	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.779097	1.019731	-0.669168	3.341299	2.673221	1.429698	-0.173842	-0.608829	0.6864	0.6978	0.3603	0.000000	0.000000
2459579	digital_ok	0.00%	0.00%	0.00%	0.00%	3.76%	0.00%	0.772438	0.813081	-0.603397	3.762028	2.891525	1.559701	0.306697	-0.058896	0.6839	0.6937	0.3596	1.569918	1.488224
2459578	digital_ok	0.00%	100.00%	100.00%	0.00%	-	-	-0.216219	1.424550	-0.786360	1.069233	0.744456	-0.857075	-0.057438	-1.018036	0.0509	0.0508	0.0066	nan	nan
2459577	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.00%	1.333974	1.432186	-0.858817	2.818795	2.410608	1.421131	0.554038	1.054414	0.6838	0.6930	0.3614	1.903710	1.777842
2459576	digital_ok	0.00%	0.00%	0.00%	0.00%	4.30%	2.15%	0.852563	1.005698	-0.686565	1.748989	1.907867	1.038815	0.125815	-1.104525	0.7093	0.7210	0.3622	1.709077	1.723484
2459575	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.521288	1.701072	-0.818977	1.801943	4.580740	1.474564	-0.616527	1.499197	0.7660	0.7607	0.3029	8.009089	7.228200
2459574	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	1.08%	1.148589	1.496631	-0.814463	1.665064	2.117258	1.941938	0.477351	0.688801	0.7016	0.7086	0.3593	1.860965	1.875259
2459573	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.765625	1.243741	-0.829360	4.212924	3.603123	1.630024	0.279381	0.614591	0.7069	0.7172	0.3550	6.409615	5.220722
2459572	digital_ok	0.00%	0.00%	0.00%	0.00%	3.78%	0.54%	0.659051	1.145294	-0.749837	1.884474	1.426141	1.114993	1.188231	1.030464	0.7096	0.7188	0.3547	1.641943	1.701667
2459571	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	0.080921	1.288186	-0.694011	0.959800	5.337638	-0.504349	0.270389	-0.847537	0.0345	0.0286	0.0016	nan	nan
2459570	digital_ok	0.00%	0.00%	0.00%	0.00%	98.92%	1.08%	0.616799	-0.975479	0.075462	-1.472869	0.500337	-0.912101	0.788847	0.630403	0.7065	0.7207	0.3266	0.000000	0.000000
2459569	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	2.70%	1.390265	1.333222	-0.789371	2.316867	-0.272962	1.900694	-0.179736	2.351060	0.7772	0.7781	0.2225	2.644847	2.468641
2459566	digital_ok	0.00%	0.00%	0.00%	0.00%	4.32%	0.00%	1.777625	1.901407	-0.640810	2.971362	2.048777	1.737147	2.092973	1.644405	0.6950	0.7051	0.3676	1.611525	1.606446
2459565	digital_ok	0.00%	-	-	-	100.00%	0.00%	0.176781	0.176781	0.138346	0.138346	0.470698	0.470698	0.005728	0.005728	nan	nan	nan	0.000000	0.000000
2459564	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.394149	-0.372267	-0.916281	-1.614750	0.650822	4.202752	1.892342	1.988205	0.7153	0.7172	0.3646	4.734152	3.837822
2459563	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	4.30%	0.779965	0.156976	-0.642537	-0.309835	2.691864	-0.738602	0.247011	-0.678709	0.7105	0.7192	0.3625	2.158690	1.954778
2459562	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.246560	-1.313076	-0.291896	-2.184658	0.881250	-1.141973	0.569266	0.284151	0.7130	0.7227	0.3571	1.977389	1.786765
2459561	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.260185	-0.926978	-0.177270	-2.329447	1.475888	-1.260918	1.025255	-0.256609	0.7115	0.7226	0.3584	1.868873	1.768568
2459560	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.390856	-1.161885	-0.057137	-2.331651	1.954340	-1.051413	0.304843	-0.687645	0.7107	0.7218	0.3681	1.887182	1.713998
2459559	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.54%	0.104185	-1.234758	-0.401786	-2.427078	1.973905	-1.349067	0.265346	-0.666759	0.7091	0.7179	0.3608	1.854053	1.695544
2459558	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	6.99%	0.810064	1.022387	-0.682704	3.243192	2.258953	0.807946	0.000365	-1.055600	0.7129	0.7191	0.3632	1.935373	1.781459
2459557	digital_ok	-	100.00%	100.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0499	0.0332	0.0048	nan	nan
2459556	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	4.86%	0.904450	1.218988	-0.774309	3.140403	3.245560	0.576534	-0.456934	-1.820834	0.7097	0.7180	0.3637	1.894332	1.763788
2459554	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7170	0.7234	0.3695	nan	nan
2459553	digital_ok	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.7129	0.7193	0.3697	nan	nan
2459552	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.478656	2.564196	-0.395148	5.516302	1.176999	3.204404	0.924978	-0.005266	0.7224	0.7215	0.3639	11.081768	10.515292
2459551	digital_ok	0.00%	0.00%	0.00%	0.00%	5.38%	0.00%	0.094237	-1.113565	-0.453180	-2.751185	2.371926	-2.200442	0.637517	-0.922771	0.7168	0.7226	0.3630	1.862818	1.686439
2459550	digital_ok	-	97.53%	97.53%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.0554	0.0461	0.0018	nan	nan
2459549	digital_ok	0.00%	0.00%	0.00%	0.00%	4.86%	0.00%	0.608618	1.413963	-0.885509	3.532110	3.655706	1.325858	0.978710	-1.193363	0.7119	0.7192	0.3737	1.987651	1.756584
2459542	digital_ok	0.00%	0.00%	0.00%	0.00%	22.32%	0.00%	1.428325	0.616707	-0.587977	2.273417	-0.165001	2.373286	1.231182	-0.861890	0.7556	0.7603	0.3334	1.988133	1.692170
2459541	digital_ok	100.00%	1.34%	1.79%	0.00%	100.00%	0.00%	1.284861	1.173404	-0.374768	4.266454	-0.466664	2.846390	0.484680	1.265759	0.7504	0.7465	0.3116	5.023345	4.517359
2459540	digital_ok	0.00%	0.00%	0.00%	0.00%	21.88%	0.45%	0.711650	-0.505645	-0.181055	-1.719589	2.842525	-2.832332	0.385894	-0.596449	0.7595	0.7611	0.3222	1.728033	1.603106
2459536	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.525104	2.340044	0.080322	6.691566	0.235809	4.517021	0.929728	-2.080780	0.7437	0.7431	0.3839	7.026992	5.719262
2459535	digital_ok	0.00%	0.00%	0.00%	0.00%	21.43%	0.00%	0.318223	-0.666341	-0.027062	-1.623230	3.202505	-2.856010	0.935727	-0.746018	0.7899	0.7865	0.3272	1.824344	1.617411
2459534	digital_ok	0.00%	0.00%	0.00%	0.00%	20.98%	0.00%	0.524129	-1.092618	0.240784	-2.237609	1.822428	-2.054288	0.388971	-0.525115	0.7814	0.7706	0.3290	1.819401	1.652448
2459533	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.291163	1.467413	-0.659629	3.712848	3.547736	1.028976	0.129461	-0.956191	0.7297	0.7402	0.3722	4.552995	3.956003
2459532	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.733357	1.689681	-0.709593	4.627882	2.821006	1.660060	0.133580	-1.223997	0.7355	0.7447	0.3656	5.623652	4.567679
2459530	digital_ok	0.00%	0.00%	0.00%	0.00%	5.92%	0.00%	0.317349	-1.008895	-0.170936	-2.111768	1.754497	-1.917811	0.063158	-0.803616	0.7507	0.7519	0.3648	1.696299	1.651418
2459527	digital_ok	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.452258	1.668220	-0.575072	4.190327	1.101957	2.914808	-0.468853	-0.855187	0.9146	0.9417	0.2745	0.000000	0.000000
2459523	digital_ok	100.00%	-	-	-	100.00%	0.00%	2.039661	1.348705	-0.835260	5.661438	0.528680	2.753784	0.037269	-1.900192	nan	nan	nan	6.434373	5.521206
2459522	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.721494	1.708970	-0.851062	4.856358	0.019976	3.632396	-0.048227	-1.431094	0.7052	0.7253	0.3163	5.985642	6.983311
2459513	digital_ok	100.00%	100.00%	100.00%	0.00%	-	-	0.094085	1.483059	-0.750099	0.455790	4.555508	0.111580	-0.020754	-0.731237	0.0624	0.0567	0.0060	nan	nan
2459508	digital_ok	0.00%	0.00%	0.00%	0.00%	81.58%	0.00%	0.507206	1.644740	-0.458200	2.684347	0.312429	0.627127	-0.264208	3.152714	0.9214	0.9482	0.1700	0.000000	0.000000
2459505	digital_ok	0.00%	0.00%	0.00%	0.00%	0.00%	0.00%	1.181076	1.024445	-0.086674	3.284905	0.289943	0.530331	-0.360774	-1.142532	0.8852	0.8527	0.1943	6.040887	4.389071
2459503	digital_ok	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.084248	3.598368	-0.056211	4.836089	0.217965	5.848069	2.390593	-1.024092	0.8916	0.8820	0.4151	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 141 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 141, Node 13:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

Antenna 141: 2459617

Antenna 141: 2459616

Antenna 141: 2459615

Antenna 141: 2459614

Antenna 141: 2459613

Antenna 141: 2459612

Antenna 141: 2459611

Antenna 141: 2459610

Antenna 141: 2459609

Antenna 141: 2459608

Antenna 141: 2459607

Antenna 141: 2459605

Antenna 141: 2459604

Antenna 141: 2459603

Antenna 141: 2459602

Antenna 141: 2459598

Antenna 141: 2459597

Antenna 141: 2459596

Antenna 141: 2459595

Antenna 141: 2459594

Antenna 141: 2459593

Antenna 141: 2459592

Antenna 141: 2459591

Antenna 141: 2459590

Antenna 141: 2459589

Antenna 141: 2459588

Antenna 141: 2459587

Antenna 141: 2459586

Antenna 141: 2459585

Antenna 141: 2459584

Antenna 141: 2459583

Antenna 141: 2459582

Antenna 141: 2459581

Antenna 141: 2459580

Antenna 141: 2459579

Antenna 141: 2459578

Antenna 141: 2459577

Antenna 141: 2459576

Antenna 141: 2459575

Antenna 141: 2459574

Antenna 141: 2459573

Antenna 141: 2459572

Antenna 141: 2459571

Antenna 141: 2459570

Antenna 141: 2459569

Antenna 141: 2459566

Antenna 141: 2459565

Antenna 141: 2459564

Antenna 141: 2459563

Antenna 141: 2459562

Antenna 141: 2459561

Antenna 141: 2459560

Antenna 141: 2459559

Antenna 141: 2459558

Antenna 141: 2459556

Antenna 141: 2459552

Antenna 141: 2459551

Antenna 141: 2459549

Antenna 141: 2459542

Antenna 141: 2459541

Antenna 141: 2459540

Antenna 141: 2459536

Antenna 141: 2459535

Antenna 141: 2459534

Antenna 141: 2459533

Antenna 141: 2459532

Antenna 141: 2459530

Antenna 141: 2459527

Antenna 141: 2459524

Antenna 141: 2459523

Antenna 141: 2459522

Antenna 141: 2459513

Antenna 141: 2459508

Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶